Design and selection of drug properties to increase the public health impact of next-generation seasonal malaria chemoprevention: a modelling study

Summary Background Seasonal malaria chemoprevention (SMC) is recommended for disease control in settings with moderate to high Plasmodium falciparum transmission and currently depends on the administration of sulfadoxine–pyrimethamine plus amodiaquine. However, poor regimen adherence and the increased frequency of parasite mutations conferring sulfadoxine–pyrimethamine resistance might threaten the effectiveness of SMC. Guidance is needed to de-risk the development of drug compounds for malaria prevention. We aimed to provide guidance for the early prioritisation of new and alternative SMC drugs and their target product profiles. Methods In this modelling study, we combined an individual-based malaria transmission model that has explicit parasite growth with drug pharmacokinetic and pharmacodynamic models. We modelled SMC drug attributes for several possible modes of action, linked to their potential public health impact. Global sensitivity analyses identified trade-offs between drug elimination half-life, maximum parasite killing effect, and SMC coverage, and optimisation identified minimum requirements to maximise malaria burden reductions. Findings Model predictions show that preventing infection for the entire period between SMC cycles is more important than drug curative efficacy for clinical disease effectiveness outcomes, but similarly important for impact on prevalence. When children younger than 5 years receive four SMC cycles with high levels of coverage (ie, 69% of children receiving all cycles), drug candidates require a duration of protection half-life higher than 23 days (elimination half-life >10 days) to achieve reductions higher than 75% in clinical incidence and severe disease (measured over the intervention period in the target population, compared with no intervention across a range of modelled scenarios). High coverage is crucial to achieve these targets, requiring more than 60% of children to receive all SMC cycles and more than 90% of children to receive at least one cycle regardless of the protection duration of the drug. Interpretation Although efficacy is crucial for malaria prevalence reductions, chemoprevention development should select drug candidates for their duration of protection to maximise burden reductions, with the duration half-life determining cycle timing. Explicitly designing or selecting drug properties to increase community uptake is paramount. Funding Bill & Melinda Gates Foundation and the Swiss National Science Foundation.


Mathematical transmission model
3][4] Here we present an overview of the relevant dynamics for the model variant used in our study, which included an explicit mechanistic model of within-host parasite dynamics 5 and incorporated flexible pharmacokinetic/pharmacodynamic (PK/PD) model for drug action. 2nMalaria generates discrete, stochastic simulations of malaria infection and transmission from individual human hosts, tracked in five-day time steps.This model, which was fully described again in Reiker and colleagues, 3 with key components listed in table A1.1, and features: seasonally forced sub-models representing malaria parasite transmission within mosquitos; stochastic predictions of parasite densities within humans, determined by a malaria infection model and acquired immunity to asexual blood stage parasitaemia, and; the ability to track and distinguish between uncomplicated and severe episodes of clinical malaria, as well as direct and indirect malaria mortality.Together, these features enable detailed simulation of malaria public healt h outcomes for different interventions, such as the impact of malaria case management, drug-based chemoprevention interventions, and vector control. 6 within-host model variant adds a mechanistic feature of parasite dynamics within the human host, where each infection is modelled with a mechanistic Plasmodium falciparum parasite model adapted from Molineaux and colleagues, 5 and incorporates full PK/PD models of intervention dynamics.This model mechanistically describes the time-course of asexual Plasmodium falciparum parasitaemia following a single inoculation within an individual human host.This time-course is driven by a parasite growth rate and by the effect of three different immune responses to parasitaemia within the human hostinnate and cross-variant immunity, acquired and variant specific immunity, and acquired and cross-variant immunityeach of which acts to reduce the parasite growth rate.By using a malaria transmission model with this realistic mathematical, within-host model of asexual parasitaemia, we capture a medical intervention's impact on both the time-course of asexual P. falciparum parasitaemia and changes to acquired immunity by modelling the drug's impact on parasite growth.

Name Description and assumptions References Key modelled epidemiological processes
Human malaria infection  Determined by EIR, a model input that affects the force of infection in the simulated setting  Human exposure to mosquitoes depends on age 1,7   Infection progression in humans: asexual parasite densities and immunity  Several within-host models of asexual parasite densities are included in OpenMalaria.
In each model, blood stage parasite density depends on the time since infection and is affected by naturally acquired immunity 1. Empirical model where expected densities of a single infection are sampled from a log-normal distribution and calibrated to malaria therapy data.The model captures between and within host variation and the duration of infection follows a log-normal distribution 8 2. Explicit mechanistic within-host model of asexual densities, capturing innate, variant-specific, and variant-transcending acquired immunity 5  Immunity (both pre-erythrocytic and blood stage) develops progressively followin g consequent episodes of exposure to infection and decays exponentially  Acquired immunity reduces parasite density of subsequent infections  Multiple distinct infections are possible with cumulative parasite densities 1,2,5,7-9 Transmission from infected humans to mosquitoes  Depends on the density of parasites present in the human, with gametocyte densities following between 10 and 20 days following asexual infection Clinical illness, morbidity , mortality and anaemia  Acute clinical illness depends on human host parasite densities and their pyrogenic threshold, which evolves over time depending on the individual exposure history  Acute morbidity episodes can be uncomplicated or can evolve to severe episodes  A proportion of the severe episodes leads to deaths  Severe disease is also induced by age-related comorbidities, resulting in indirect severe disease and mortality

Case management
Results for two levels of health system access for first-line treatment of malaria were captured:  A low probability of seeking care for clinical illness (10% within 14 days). A high probability of seeking care for clinical illness (50% within 14 days).Treatment was assumed to clear all blood stage infections for one time-step.

Primary outcomes
We measured next-generation SMC's impact on three relative outcomes in the target population:  Clinical incidence reduction: The percentage reduction in clinical incidence compared with a no intervention counterfactual, where incidence was defined as the number of new, uncomplicated malaria cases across the three-, four-, or five-month intervention period in the fifth year after deployment.Uncomplicated malaria was defined as an episode of symptomatic malaria, detectable by rapid diagnostic test with 94.2% specificity and a detection limit of 50 parasites per microlitre, where symptoms did not qualify as severe malaria. Prevalence reduction: The percentage reduction in prevalence compared with a no intervention counterfactual, where prevalence was defined as the proportion of all malaria infections (detectable by rapid diagnostic test or not) at the end of the three-, four-, or five-month intervention period in the fifth year after deployment. Severe disease reduction: The percent reduction in the number of severe malaria cases compared with a no intervention counterfactual, where severe cases of malaria were evaluated across the three -, four-, or five-month intervention period in the fifth year after deployment.An episode of severe malaria was an episode of symptomatic malaria, detectable by rapid diagnostic test with 94.2% specificity and a detection limit of 50 parasites per microlitre, with symptoms qualifying as severe malaria or with comorbidities 1 .All outcomes were evaluated in the intervention's target population: children aged three to 59 or three to 119 months.

Seasonality
Differences in SMC's performance were captured across types of seasonal transmission by reporting results for archetypal seasonal profiles, where approximately 70% of cases occurred within three or five months in a given year (figure A1.2).

SMC cycles
The importance of covering the entire length of a region's highest risk period to malaria infection was evaluated by varying the number of cycles of SMC:  Three cycles of SMC, where the first cycle was deployed one month prior to peak malaria transmission, with additional cycles deployed in monthly intervals for the two subsequent cycles. Four cycles of SMC, where the first cycle was deployed one month prior to peak malaria transmission, with additional cycles deployed in monthly intervals for the three subsequent cycles. Five cycles of SMC, where the first cycle was deployed two months prior to peak malaria transmission, with additional cycles deployed in monthly intervals for the three subsequent months.

Target population
SMC was evaluated for two target populations:  Children three to 59 months (5 years) old.
 Children three to 119 months (10 years) old.Transmission intensity SMC's performance was explored across a variety of malaria-endemic regions by providing results across a range of transmission intensities, from low (8%) to high (39%) annual baseline annual prevalence rate of P. falciparum patent infections (detectable by rapid diagnostic test with 94.2% specificity and a detection limit of 50 parasites per microlitre) in children aged two to ten years of age (PfPR 2-10 ) compared with the no SMC intervention counterfactual.

Next-generation SMC with blood stage activity only
To model next-generation SMC drugs with blood stage activity only, we used a one-compartment pharmacokinetic/pharmacodynamic (PK/PD) model to describe a next-generation SMC drug's killing effect at time t as where C(t) is the drug's concentration in mg/liter, Emax the maximum parasite killing rate per day, EC50 the concentration at which 50% of the maximum killing rate occurs in mg/liter, and n the slope of the drug's dose response curve. 21The drug's concentration decays over time t with Where C0 is the drug's concentration at time zero and t1/2 is the elimination half-life.
For simplicity, given that pharmacokinetic properties for next-generation SMC are still unknown, we assumed a piperaquine-like treatment schedule with a single dose administered daily for three days at a weight-based dosing schedule of 18 mg/kg. 22Parameter values for the model's volume of distribution and EC50 were selected to represent piperaquine-like behavior, 23 as detailed in table 1.
We captured uncertainty around next-generation SMC's pharmacological properties by varying key parameters for drug efficacy and duration.To capture uncertainty about drug efficacy, we varied the Emax between two and 30.To represent uncertainty in a drug's duration of protection, we ranged the elimination half-life t1/2 between five and 40 days.In this one-compartment PK/PD, uncertainty in drug duration could equally have been represented by ranging the EC50.We varied the EC50 in our initial analyses and elected to present results for a fixed value.This modelling decision was made in order to limit the complexity of performing optimisation on two parameters for duration.The slope n was also varied in initial analyses but was found to have minimal contribution to model outcomes, which were presented for a slope of six.
The PK component of this model has only one compartment and assumes instantaneous absorption and, as such, may not reflect the complexity of a chemoprevention drug's exposure-response relationship.However, this simple approach to modelling SMC captured a next-generation drug's potential for activity against blood stage parasites without making complex assumptions about that drug's as-yet-unknown PK properties.

Next-generation SMC with dominant blood stage activity and initial, complete liver stage clearance
Some next-generation SMC candidates are likely to be partially active against liver stage parasites.For this reason, our 'dominant blood stage activity' model deployed the PK/PD model described above with the addition of one time-step of liver stage parasite clearance from the start of SMC administration.

Next-generation SMC with dominant liver stage activity and initial, complete blood stage clearance
To capture next-generation SMC's likely protective effect against liver stage parasites, we modelled the intervention's protective effect as a probability of preventing liver stage infection that decays over time, in combination with five days of blood and liver stage parasite clearance from the start of SMC administration.This approach, which was based on a previously calibrated model of SP-AQ, 24 has two key parameters: duration of protection, driven by the drug's elimination half-life and EC50, and; initial probability of preventing infection, driven by the drug's maximal effect Emax.By modelling the intervention's protective effect over time, we could translate a drug's pharmacokinetics or pharmacodynamics to its expected public health impact.As for nextgeneration SMC with blood stage activity, we modelled a range of values for these key characteristics (table 1).

In-silico clinical trial modelling for the standard-of-care
We assessed each model's ability to accurately represent the dynamics of SP-AQ through in-silico clinical trial modelling, where we replicated SP-AQ's protective efficacy against clinical cases from a randomised noninferiority trial of dihydroartemisinin-piperaquine (DHA-PPQ) to SP-AQ, conducted between 2009 and 2010 in Burkina Faso by Zongo and colleagues. 22Adapting Burgert and colleagues' previous parameterization of SP-AQ to this same trial, 24 using the following approach: 1. Protective efficacy data for SP-AQ against clinical cases was extracted from figure 3 of Zongo and colleagues, and the trial was re-built in OpenMalaria using the settings described in table A1.3.2. For each of the three mechanism-of-action models, we used latin-hypercube sampling to generate 500 samples of parameter values within the ranges described in table 1. 3. For each potential mechanism-of-action and for each parameter sample, we ran ten stochastic replicates of the in-silico clinical trial with OpenMalaria, simulating both the intervention arm of the trial and a no-intervention control arm.4. Protective efficacy was calculated per time-step as where clinical cases were detectable by rapid diagnostic test. 5. Protective efficacy for each potential mechanism-of-action was considered to be sufficiently close to that of SP-AQ if the residual sum of squares (RSS) was within 0•1 standard deviations of the minimum RSS.

Mosquito species
Anopheles funestus: 42% indoor biting and 31% outdoor biting Anopheles gambiae: 14% indoor biting and 13% outdoor biting Total entomological innoculation rate (EIR) 350 for in-silico modelling of blood stage mechanism-of-actions 154 for in-silico modelling of liver stage mechanism-of-actions ..

Access to treatment
Effective coverage within 14-days of 50%, represented in OpenMalaria as 24.12% over 5day time-steps Following our previous methodology 20 and as shown in figure A1.3, our predictive target product profile framework began with simulation from the individual-based malaria transmission model over a discrete sample of input intervention parameters.1000 samples were uniformly generated with Latin hypercube sampling and, for each sample, simulations were generated with 5 stochastic replicates.Following simulation, we used the hetgp package 28 to fit a heteroskedastic Gaussian Process regression model to emulate the relationship between input intervention parameters and their corresponding simulated outcomes.This step trained a computationally light model emulator of the individual-based malaria transmission model's complex, deterministic dynamics.Emulator performance was assessed using the R-squared correlation coefficient to evaluate correlation between true and predicted outcomes against a 10% hold-out set.
We then undertook a nonparametric variance-based sensitivity analysis of our model results to identify the extent to which a small change in an SMC drug's properties contributed to a change in its effectiveness.This was performed with the sensitivity package 29 , using the Sobol-Jansen method 30 to compute Sobol total-order indices for two uniform samples of 50 000 input intervention parameters, generated with Latin hypercube sampling with 1000 bootstrap replicates.Sobol total-order indices represent the contribution of a given input variable to the variance of the output, called total-order or total-effect because this contribution includes any contribution to the variance by interactions with other input variables.
To identify desirable product characteristics for SMC, we linked a desired public health outcome with its required minimum intervention properties using an optimisation grid search.This was performed for blood stage models only by evaluating emulator predictions of public health outcomes on a sample of intervention properties (10 000 samples generated with Latin hypercube sampling) and identifying the estimated minimum property value whose 95% prediction interval was above the given target reduction in both clinical incidence and severe disease.This minimum was then aggregated by calculating the most conservative (maximum) value across outcomes (clinical incidence and severe disease reduction measured across the intervention period), SMC deployments (three, four, and five monthly cycles of SMC in a given year surrounding peak seasonality) and levels for other model parameters (elimination half-life and Emax).All analyses were conducted in R. 31  Each panel shows the predicted reduction when all performance characteristics but the parameter of interest were held constant at: 80% round coverage, 80% cycle coverage, an elimination half-life of 5 days, and a maximum parasite killing rate of 2 units.
(B) Results show 50% access to first-line treatment within 14 days and a five-month seasonal profile with baseline annual PfPR2-10 of 18% when SMC was deployed in four monthly cycles to children aged three to 59 months.Each panel shows the predicted reductionevaluated in SMC naïve children aged 60 to 119 months in the first intervention yearwhen all performance characteristics but the parameter of interest were held constant at: 95% round coverage, 95% cycle coverage, an elimination half-life of 15 days, and a maximum parasite killing rate of 10 units.Each panel shows the predicted reductionevaluated in the first intervention yearwhen all performance characteristics but the parameter of interest were held constant at: 90% round coverage, 90% cycle coverage, an elimination half-life of 10 days, and a maximum parasite killing rate of 3.45 units.
(B) Results show 50% access to first-line treatment within 14 days and a five-month seasonal profile with baseline annual PfPR2-10 of 18% when SMC was deployed in four monthly cycles to children aged three to 59 months.Each panel shows the predicted reductionevaluated in SMC naïve children aged 60 to 119 months in the first intervention yearwhen all performance characteristics but the parameter of interest were held constant at: 90% round coverage, 90% cycle coverage, an elimination half-life of 10 days, and a maximum parasite killing rate of 3.45 units.Figure A2.11:For SMC with blood stage activity only, impact of a change in coverage on the predicted relative reduction in clinical incidence (measured over a four-month intervention period) following SMC compared with a no intervention counterfactual Each square in the grid indicates the predicted reduction (rounded to the nearest 10%) if an interventio n with the given elimination half-life (horizontal axis) and maximum parasite killing rate (vertical axis) were deployed, assuming a slope of six.Variation in this figure is driven by the combined impact of stochastic uncertainty and emulator prediction error.Results are shown for children aged three and 59 months for a five-month seasonal profile with an baseline annual PfPR2-10 of 18%, where access to first-line treatment was 50% within 14 days and where SMC was deployed four times at monthly intervals in a given year surrounding peak seasonality.Each panel represents results for a different level of SMC round coverage (75%, 85%, and 95%) and cycle coverage (75%, 85%, and 95%).The white region indicates the space of parameter profiles whose RSS (in-silico protective efficacy calculated in comparison to SP-AQ's protective efficacy) falls within 0.1 standard deviations of the minimum (see figure 1). Figure A2.12:For SMC with dominant liver stage activity and initial, complete blood stage clearance, impact of a change in coverage on the predicted relative reduction in clinical incidence (measured over a four-month intervention period) following SMC compared with a no intervention counterfactual Each square in the grid indicates the predicted reduction (rounded to the nearest 10%) if an intervention with the given duration of protection half-life (horizontal axis) and initial efficacy (vertical axis) were deployed.Variation in this figure is driven by the combination of combined impact of stochastic uncertainty and emulator prediction error.Results are shown for children aged three and 59 months for a five-month seasonal profile with an baseline annual PfPR2-10 of 18%, where access to first-line treatment was 50% within 14 days and where SMC was deployed four times at monthly intervals in a given year surrounding peak seasonality.Each panel represents results for a different level of SMC round coverage (75%, 85%, and 95%) and cycle coverage (75%, 85%, and 95%).The white region indicates the space of parameter profiles whose RSS (in-silico protective efficacy calculated in comparison to SP-AQ's protective efficacy) falls within 0.1 standard deviations of the minimum (see figure 1). Figure A2.13:Predicted minimum elimination half-life values for SMC with dominant blood stage activity and initial, complete liver stage clearance to achieve target reductions in clinical incidence and severe disease Summary of the predicted minimum elimination half-life criteria for SMC with dominant blood stage activity and initial, complete liver stage clearance towards achieving a target clinical incidence reduction for varying levels of SMC round and cycle coverage, shown for a five-month seasonal profile in a setting with 50% access to first-line treatment, where SMC was deployed in four monthly cycles to children three to 59 months old.Results show the estimated minimum elimination half-life with a 95% prediction interval above the given target reduction in clinical incidence (vertical axis), compared with a no intervention counterfactual and measured across the four-month intervention period.Minimum criteria were calculated across Emax levels.

Figure A1. 3 : 1 :
Figure A1.3:Evidence generation framework combining individual -based malaria transmission modelling with statistical methods to support the identification of minimum necessary SMC product characteristics

Figure A2. 9 :
Figure A2.9:For SMC with dominant blood stage activity and initial, complete liver stage clearance, (A) illustrative Gaussian Process regression emulator predictions for the relationship between SMC properties and their direct effects on clinical incidence, prevalence, and severe disease reduction for a drug profile with low coverage and low protective efficacy, and (B) illustrative Gaussian Process regression emulator predictions for the relationship between SMC properties and their indirect effects on clinical incidence, prevalence, and severe disease reduction for a drug profile with high coverage and high protective efficacy(A) Results show 50% access to first-line treatment within 14 days and a five-month seasonal profile with baseline annual PfPR2-10 of 18% when SMC was deployed in four monthly cycles to children aged three to 59 months.Each panel shows the predicted reduction when all performance characteristics but the parameter of interest were held constant at: 80% round coverage, 80% cycle coverage, an elimination half-life of 5 days, and a maximum parasite killing rate of 2 units.

Figure A2. 10 :
Figure A2.10:For SMC with dominant blood stage activity and initial, complete liver stage clearance, (A) illustrative Gaussian Process regression emulator predictions for the relationship between SMC properties and their direct effects on clinical incidence, prevalence, and severe disease reduction in children aged three to 59 months receiving SMC, and (B) illustrative Gaussian Process regression emulator predictions for the relationship between SMC properties and their indirect effects on clinical incidence, prevalence, and severe disease reduction in children aged 60 to 119 months not receiving SMC (A) Results show 50% access to first-line treatment within 14 days and a five-month seasonal profile with baseline annual PfPR2-10 of 18% when SMC was deployed in four monthly cycles to children aged three to 59 months.Each panel shows the predicted reductionevaluated in the first intervention yearwhen all performance characteristics but the parameter of interest were held constant at: 90% round coverage, 90% cycle coverage, an elimination half-life of 10 days, and a maximum parasite killing rate of 3.45 units.

Table A1 .1: Summary of disease model characteristics, adapted from Golumbeanu et al. 20 Scenarios and outcome measures Term Scenario
Model dynamics and outputs are not separated by sex or gender, due to the lack of appropriate data for model calibration to these characteristics Transmission seasonality  Seasonally forced, the same transmission pattern is reproduced each year in absence of interventions